Exploració per autor "Belanche Muñoz, Luis Antonio"
Ara es mostren els items 19-38 de 84
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Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning
(Institute of Electrical and Electronics Engineers (IEEE), 2012)
Text en actes de congrés
Accés obertIn neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due ... -
Desarrollo integral de las competencias genéricas mediante mapas competenciales
(Universidad de Castilla-La Mancha, 2012-07-10)
Comunicació de congrés
Accés obertLos planes de estudio del EEES deben diseñarse a partir de las competencias de la titulación, tanto específicas como genéricas. La universidad española tiene una amplia experiencia en trabajar y evaluar las competencias ... -
Developing professional skills at tertiary level: A model to integrate competencies across the curriculum
(Institute of Electrical and Electronics Engineers (IEEE), 2014)
Text en actes de congrés
Accés obertIn the context of the European Higher Education Area, curriculum design needs to be based on the defined competencies of each degree programs, including both domain specific and professional competencies. In this educational ... -
Developments in kernel design
(2013)
Text en actes de congrés
Accés obertThe aim of this paper is to give a concise overview of kernels, with a special attention to non-standard or heterogeneous data sources (e.g. non-numerical or structured data). A second goal is to discuss the world of ... -
Differentiation of glioblastomas and metastases using 1H-MRS spectral data
(CSREA Press, 2010)
Text en actes de congrés
Accés obertHydrogen-1 magnetic resonance spectroscopy (1H-MRS) allows noninvasive in vivo quantification of metabolite concentrations in brain tissue. In this work two of the most aggressive brain tumors are studied with the purpose ... -
Distance-based kernels for real-valued data
(Springer, 2008)
Text en actes de congrés
Accés obertWe consider distance-based similarity measures for real-valued vectors of interest in kernel-based machine learning algorithms. In particular, a truncated Euclidean similarity measure and a self-normalized similarity measure ... -
Effective classification and gene expression profiling for the facioscapulohumeral muscular dystrophy
(2013-12-13)
Article
Accés obertThe Facioscapulohumeral Muscular Dystrophy (FSHD) is an autosomal dominant neuromuscular disorder whose incidence is estimated in about one in 400,000 to one in 20,000. No effective therapeutic strategies are known to halt ... -
Evolutionary optimization of heterogeneous problems
(2003-02)
Report de recerca
Accés obertA large number of practical optimization problems involve elements of quite diverse nature, described as mixtures of qualitative and quantitative information, and whose description is possibly incomplete. In this work we ... -
Evolutionary optimization of heterogeneous problems
(Springer, 2002)
Text en actes de congrés
Accés obertA large number of practical optimization problems involve elements of quite diverse nature described as mixtures of qualitative and quantitative information and whose description is possibly incomplete. In this work we ... -
Exploiting the accumulated evidence for gene selection in microarray gene expression data
(IOS Press, 2010)
Text en actes de congrés
Accés obertFeature subset selection (FSS) methods play an important role for cancer classification using microarray gene expression data. In this scenario, it is extremely important to select genes by taking into account the possible ... -
Exploiting the accumulated evidence for gene selection in microarray gene expression data
(2013)
Report de recerca
Accés obertMachine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an ... -
Extended linear models with Gaussian prior on the parameters and adaptive expansion vectors
(Springer, 2007)
Text en actes de congrés
Accés obertWe present an approximate Bayesian method for regression and classification with models linear in the parameters. Similar to the Relevance Vector Machine (RVM), each parameter is associated with an expansion vector. Unlike ... -
Feature and model selection in 1H-MRS single voxel spectra for cancer classification
(Future Technology Press, 2009)
Capítol de llibre
Accés restringit per política de l'editorialMachine learning is a powerful paradigm within which to analyze 1HMRS spectral data for the classification of tumour pathologies. An important characteristic of this task is the high dimensionality of the involved data ... -
Feature selection algorithms: a survey and experimental evaluation
(2003-02)
Report de recerca
Accés obertIn view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work reviews several ... -
Feature selection algorithms: a survey and experimental evaluation
(Institute of Electrical and Electronics Engineers (IEEE), 2002)
Text en actes de congrés
Accés obertIn view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work assesses the ... -
Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy
(2013)
Report de recerca
Accés obertIn this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. ... -
Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy
(2014-04-01)
Article
Accés obertMicroarray classification poses many challenges for data analysis, given that a gene expression data set may consist of dozens of observations with thousands or even tens of thousands of genes. In this context, feature ... -
Feature selection for the prediction and visualization of brain tumor types using proton magnetic resonance spectroscopy data
(Springer, 2012)
Capítol de llibre
Accés restringit per política de l'editorialIn cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of basic tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ... -
Feature selection in proton magnetic resonance spectroscopy data of brain tumors
(Università degli Studi di Salerno, 2011)
Text en actes de congrés
Accés obertIn cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ... -
Feature selection in proton magnetic resonance spectroscopy for brain tumor classification
(2008)
Text en actes de congrés
Accés obertH-MRS is a technique that uses response of protons under certain magnetic conditions to reveal the biochemical structure of human tissue. An important application is found in brain tumor diagnosis, due to the known ...
